@inproceedings{8aebf7b8e7f249d28ed95ad9ea183605,
title = "Simulation-based predictive analytics for dynamic queueing systems",
abstract = "Simulation and simulation optimization have primarily been used for static system design problems based on long-run average performance measures. Control or policy-based optimization has been a weakness, because it requires a way to predict future behavior based on current state and time information. This work is a first step in that direction with a focus on congestion measures for queueing systems. The idea is to fit predictive models to dynamic sample paths of the system state from a detailed simulation. We propose a two-step method to dynamically predict the probability that the system state belongs to a certain subset and test the performance of this method on two examples.",
author = "Huiyin Ouyang and Nelson, {Barry L.}",
note = "Funding Information: This research is supported by the National Science Foundation Grant CMMI-1537060 and SAS Institute. Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 Winter Simulation Conference, WSC 2017 ; Conference date: 03-12-2017 Through 06-12-2017",
year = "2017",
month = jun,
day = "28",
doi = "10.1109/WSC.2017.8247910",
language = "English (US)",
series = "Proceedings - Winter Simulation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1716--1727",
editor = "Victor Chan",
booktitle = "2017 Winter Simulation Conference, WSC 2017",
address = "United States",
}